Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches

Yixin Yang; Kexuan Liu; Yunhui Gao; Chen Wang; Liangcai Cao

doi:10.1038/s41377-025-01923-w

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Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches

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- Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches
- Light: Science & Applications Vol. 14, Issue 9, Pages: 2384-2404(2025)
- Affiliations：
  
  1.Department of Precision Instruments, Tsinghua University, Beijing 100084, China
  2.School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- Author bio：
  
  Liangcai Cao (clc@tsinghua.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01923-w
  CLC：
- Received：27 December 2024，
  
  Revised：2025-04-07，
  
  Accepted：18 June 2025，
  
  Published Online：24 July 2025，
  
  Published：30 September 2025
- Accepted：
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Yang, Y. X. et al. Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches. Light: Science & Applications, 14, 2384-2404 (2025).
DOI：

Yang, Y. X. et al. Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches. Light: Science & Applications, 14, 2384-2404 (2025). DOI： 10.1038/s41377-025-01923-w.

摘要

Abstract

Inverse lithography technology (ILT) is a promising approach in computational lithography to address the challenges posed by shrinking semiconductor device dimensions. The ILT leverages optimization algorithms to generate mask patterns

outperforming traditional optical proximity correction methods. This review provides an overview of ILT’s principles

evolution

and applications

with an emphasis on integration with artificial intelligence (AI) techniques. The review tracks recent advancements of ILT in model improvement and algorithmic efficiency. Challenges such as extended computational runtimes and mask-writing complexities are summarized

with potential solutions discussed. Despite these challenges

AI-driven methods

such as convolutional neural networks

deep neural networks

generative adversarial networks

and model-driven deep learning methods

are transforming ILT. AI-based approaches offer promising pathways to overcome existing limitations and support the adoption in high-volume manufacturing. Future research directions are explored to exploit ILT’s potential and drive progress in the semiconductor industry.

关键词

Keywords

references

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